These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

270 related articles for article (PubMed ID: 33536499)

  • 21. The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.
    Xu Y; He X; Li Y; Pang P; Shu Z; Gong X
    J Magn Reson Imaging; 2021 Aug; 54(2):571-583. PubMed ID: 33559302
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
    Hashido T; Saito S; Ishida T
    J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):606-613. PubMed ID: 34270479
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Cs-131 brachytherapy for patients with recurrent glioblastoma combined with bevacizumab avoids radiation necrosis while maintaining local control.
    Wernicke AG; Taube S; Smith AW; Herskovic A; Parashar B; Schwartz TH
    Brachytherapy; 2020; 19(5):705-712. PubMed ID: 32928486
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation.
    Lee S; Lee SY; Jung JY; Nam Y; Jeon HJ; Jung CK; Shin SH; Chung YG
    PLoS One; 2023; 18(5):e0286417. PubMed ID: 37256875
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.
    Park YW; Oh J; You SC; Han K; Ahn SS; Choi YS; Chang JH; Kim SH; Lee SK
    Eur Radiol; 2019 Aug; 29(8):4068-4076. PubMed ID: 30443758
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.
    Chen X; Fang M; Dong D; Liu L; Xu X; Wei X; Jiang X; Qin L; Liu Z
    Acad Radiol; 2019 Oct; 26(10):1292-1300. PubMed ID: 30660472
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Toward distinguishing recurrent tumor from radiation necrosis: DWI and MTC in a Gamma Knife--irradiated mouse glioma model.
    Perez-Torres CJ; Engelbach JA; Cates J; Thotala D; Yuan L; Schmidt RE; Rich KM; Drzymala RE; Ackerman JJ; Garbow JR
    Int J Radiat Oncol Biol Phys; 2014 Oct; 90(2):446-53. PubMed ID: 25104071
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Uninterpretable Dynamic Susceptibility Contrast-Enhanced Perfusion MR Images in Patients with Post-Treatment Glioblastomas: Cross-Validation of Alternative Imaging Options.
    Heo YJ; Kim HS; Park JE; Choi CG; Kim SJ
    PLoS One; 2015; 10(8):e0136380. PubMed ID: 26296086
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
    Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
    Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging.
    Chu HH; Choi SH; Ryoo I; Kim SC; Yeom JA; Shin H; Jung SC; Lee AL; Yoon TJ; Kim TM; Lee SH; Park CK; Kim JH; Sohn CH; Park SH; Kim IH
    Radiology; 2013 Dec; 269(3):831-40. PubMed ID: 23771912
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images.
    Zhang Q; Cao J; Zhang J; Bu J; Yu Y; Tan Y; Feng Q; Huang M
    Comput Math Methods Med; 2019; 2019():2893043. PubMed ID: 31871484
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions.
    Zhang Q; Peng Y; Liu W; Bai J; Zheng J; Yang X; Zhou L
    J Magn Reson Imaging; 2020 Aug; 52(2):596-607. PubMed ID: 32061014
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.
    Du D; Feng H; Lv W; Ashrafinia S; Yuan Q; Wang Q; Yang W; Feng Q; Chen W; Rahmim A; Lu L
    Mol Imaging Biol; 2020 Jun; 22(3):730-738. PubMed ID: 31338709
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.
    Zhang B; Lian Z; Zhong L; Zhang X; Dong Y; Chen Q; Zhang L; Mo X; Huang W; Yang W; Zhang S
    BMC Cancer; 2020 Jun; 20(1):502. PubMed ID: 32487085
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma.
    Alexiou GA; Zikou A; Tsiouris S; Goussia A; Kosta P; Papadopoulos A; Voulgaris S; Tsekeris P; Kyritsis AP; Fotopoulos AD; Argyropoulou MI
    Magn Reson Imaging; 2014 Sep; 32(7):854-9. PubMed ID: 24848292
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.
    Saini J; Kumar Gupta P; Awasthi A; Pandey CM; Singh A; Patir R; Ahlawat S; Sadashiva N; Mahadevan A; Kumar Gupta R
    Clin Radiol; 2018 Nov; 73(11):986.e7-986.e15. PubMed ID: 30197047
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
    Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
    Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
    [TBL] [Abstract][Full Text] [Related]  

  • 38. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.
    Zhang Z; Yang J; Ho A; Jiang W; Logan J; Wang X; Brown PD; McGovern SL; Guha-Thakurta N; Ferguson SD; Fave X; Zhang L; Mackin D; Court LE; Li J
    Eur Radiol; 2018 Jun; 28(6):2255-2263. PubMed ID: 29178031
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
    Nakagawa M; Nakaura T; Namimoto T; Kitajima M; Uetani H; Tateishi M; Oda S; Utsunomiya D; Makino K; Nakamura H; Mukasa A; Hirai T; Yamashita Y
    Eur J Radiol; 2018 Nov; 108():147-154. PubMed ID: 30396648
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility.
    Kim HS; Goh MJ; Kim N; Choi CG; Kim SJ; Kim JH
    Radiology; 2014 Dec; 273(3):831-43. PubMed ID: 24885857
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 14.